


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_SCREAMING_SNAKE_CASE     :					Dict      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					Any      =       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

_SCREAMING_SNAKE_CASE     :					Optional[int]      =       {
    "roberta-base": 5_12,
    "roberta-large": 5_12,
    "roberta-large-mnli": 5_12,
    "distilroberta-base": 5_12,
    "roberta-base-openai-detector": 5_12,
    "roberta-large-openai-detector": 5_12,
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						VOCAB_FILES_NAMES
    __magic_name__         =						PRETRAINED_VOCAB_FILES_MAP
    __magic_name__         =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__         =						['input_ids', 'attention_mask']
    __magic_name__         =						RobertaTokenizer
    def __init__(			self     ,   __snake_case=None     ,   __snake_case=None     ,   __snake_case=None     ,   __snake_case="replace"     ,   __snake_case="<s>"     ,   __snake_case="</s>"     ,   __snake_case="</s>"     ,   __snake_case="<s>"     ,   __snake_case="<unk>"     ,   __snake_case="<pad>"     ,   __snake_case="<mask>"     ,   __snake_case=False     ,   __snake_case=True     ,   **__snake_case     ,   ):
         super().__init__(
             __snake_case     ,   __snake_case     ,   tokenizer_file=__snake_case     ,   errors=__snake_case     ,   bos_token=__snake_case     ,   eos_token=__snake_case     ,   sep_token=__snake_case     ,   cls_token=__snake_case     ,   unk_token=__snake_case     ,   pad_token=__snake_case     ,   mask_token=__snake_case     ,   add_prefix_space=__snake_case     ,   trim_offsets=__snake_case     ,   **__snake_case     ,   )

         snake_case											=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
         if pre_tok_state.get('''add_prefix_space'''     ,   __snake_case       ) != add_prefix_space:
              snake_case											=							getattr(__snake_case     ,   pre_tok_state.pop('''type'''       )       )
              snake_case											=							add_prefix_space
              snake_case											=							pre_tok_class(**__snake_case       )

         snake_case											=							add_prefix_space

         snake_case											=							'''post_processor'''
         snake_case											=							getattr(self.backend_tokenizer     ,   __snake_case     ,   __snake_case       )
         if tokenizer_component_instance:
              snake_case											=							json.loads(tokenizer_component_instance.__getstate__()       )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   snake_case											=							tuple(state['''sep''']       )
              if "cls" in state:
                   snake_case											=							tuple(state['''cls''']       )

              snake_case											=							False

              if state.get('''add_prefix_space'''     ,   __snake_case       ) != add_prefix_space:
                   snake_case											=							add_prefix_space
                   snake_case											=							True

              if state.get('''trim_offsets'''     ,   __snake_case       ) != trim_offsets:
                   snake_case											=							trim_offsets
                   snake_case											=							True

              if changes_to_apply:
                   snake_case											=							getattr(__snake_case     ,   state.pop('''type'''       )       )
                   snake_case											=							component_class(**__snake_case       )
                   setattr(self.backend_tokenizer     ,   __snake_case     ,   __snake_case       )
    @property
    def 						a_     (			self       ):
         if self._mask_token is None:
              if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''       )
              return None
         return str(self._mask_token       )
    @mask_token.setter
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else value
         snake_case											=							value
    def 						a_     (			self     ,   *__snake_case     ,   **__snake_case       ):
         snake_case											=							kwargs.get('''is_split_into_words'''     ,   __snake_case       )
         assert self.add_prefix_space or not is_split_into_words, (
             F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__snake_case     ,   **__snake_case       )
    def 						a_     (			self     ,   *__snake_case     ,   **__snake_case       ):
         snake_case											=							kwargs.get('''is_split_into_words'''     ,   __snake_case       )

         assert self.add_prefix_space or not is_split_into_words, (
             F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__snake_case     ,   **__snake_case       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							self._tokenizer.model.save(__snake_case     ,   name=__snake_case       )
         return tuple(__snake_case       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case=None       ):
         snake_case											=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							[self.sep_token_id]
         snake_case											=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE     :					int      =       logging.getLogger()
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							argparse.ArgumentParser()
     parser.add_argument('''-f'''							)
     snake_case											=							parser.parse_args()
     return args.f

class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							logging.StreamHandler(sys.stdout       )
         logger.addHandler(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0     ,   '''run_glue_deebert.py'''       )
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   snake_case											=							run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(__snake_case     ,   0.666       )




    @slow
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )




